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Article ; Online: Exscalate4CoV: Innovative High Performing Computing (HPC) Strategies to Tackle Pandemic Crisis.

Beccari, Andrea R / Vistoli, Giulio

International journal of molecular sciences

2022 Volume 23, Issue 19

Abstract: This Special Issue was intended as a dissemination forum where the major results pursued by the EXSCALATE4CoV project (E4C, https://www [ ... ]. ...

Abstract	This Special Issue was intended as a dissemination forum where the major results pursued by the EXSCALATE4CoV project (E4C, https://www [...].
MeSH term(s)	Computing Methodologies ; Pandemics/prevention & control ; Software
Language	English
Publishing date	2022-09-30
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms231911576
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Ensemble of structure and ligand-based classification models for hERG liability profiling.

Vittorio, Serena / Lunghini, Filippo / Pedretti, Alessandro / Vistoli, Giulio / Beccari, Andrea R

Frontiers in pharmacology

2023 Volume 14, Page(s) 1148670

Abstract: Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is ... ...

Abstract	Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.
Language	English
Publishing date	2023-03-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587355-6
ISSN	1663-9812
ISSN	1663-9812
DOI	10.3389/fphar.2023.1148670
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Article ; Online: Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel.

Gervasoni, Silvia / Talarico, Carmine / Manelfi, Candida / Pedretti, Alessandro / Vistoli, Giulio / Beccari, Andrea R

International journal of molecular sciences

2022 Volume 23, Issue 14

Abstract: 1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their ... ...

Abstract	(1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the best structures can be selected based on available experimental data. Without experimental templates, the MD trajectories can be filtered by energy-based criteria or sampled by systematic analyses. (2) Methods: A blind and methodical analysis was performed on the already reported MD run of the hTRPM8 tetrameric structures; a total of 50 frames underwent docking simulations by using a set of 1000 ligands including 20 known hTRPM8 modulators. Docking runs were performed by LiGen program and involved the frames as they are and after optimization by SCRWL4.0. For each frame, all four monomers were considered. Predictive models were developed by the EFO algorithm based on the sole primary LiGen scores. (3) Results: On average, the MD simulation progressively enhances the performance of the extracted frames, and the optimized structures perform better than the non-optimized frames (EF1% mean: 21.38 vs. 23.29). There is an overall correlation between performances and volumes of the explored pockets and the combination of the best performing frames allows to develop highly performing consensus models (EF1% = 49.83). (4) Conclusions: The systematic sampling of the entire MD run provides performances roughly comparable with those previously reached by using rationally selected frames. The proposed strategy appears to be helpful when the lack of experimental data does not allow an easy selection of the optimal structures for docking simulations. Overall, the reported docking results confirm the relevance of simulating all the monomers of an oligomer structure and emphasize the efficacy of the SCRWL4.0 method to optimize the protein structures for docking calculations.
MeSH term(s)	Binding Sites ; Ligands ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Protein Binding ; Proteins/chemistry
Chemical Substances	Ligands ; Proteins
Language	English
Publishing date	2022-07-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms23147558
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Article ; Online: Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative.

Gervasoni, Silvia / Manelfi, Candida / Adobati, Sara / Talarico, Carmine / Biswas, Akash Deep / Pedretti, Alessandro / Vistoli, Giulio / Beccari, Andrea R

International journal of molecular sciences

2023 Volume 25, Issue 1

Abstract: Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. ... ...

Abstract	Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.
MeSH term(s)	Humans ; COVID-19/diagnosis ; SARS-CoV-2 ; Consensus
Language	English
Publishing date	2023-12-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms25010450
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Article ; Online: Structure of human TRPM8 channel.

Palchevskyi, Sergii / Czarnocki-Cieciura, Mariusz / Vistoli, Giulio / Gervasoni, Silvia / Nowak, Elżbieta / Beccari, Andrea R / Nowotny, Marcin / Talarico, Carmine

Communications biology

2023 Volume 6, Issue 1, Page(s) 1065

Abstract: TRPM8 is a non-selective cation channel permeable to both monovalent and divalent cations that is activated by multiple factors, such as temperature, voltage, pressure, and changes in osmolality. It is a therapeutic target for anticancer drug development, ...

Abstract	TRPM8 is a non-selective cation channel permeable to both monovalent and divalent cations that is activated by multiple factors, such as temperature, voltage, pressure, and changes in osmolality. It is a therapeutic target for anticancer drug development, and its modulators can be utilized for several pathological conditions. Here, we present a cryo-electron microscopy structure of a human TRPM8 channel in the closed state that was solved at 2.7 Å resolution. Our structure comprises the most complete model of the N-terminal pre-melastatin homology region. We also visualized several lipids that are bound by the protein and modeled how the human channel interacts with icilin. Analyses of pore helices in available TRPM structures showed that all these structures can be grouped into different closed, desensitized and open state conformations based on the register of the pore helix S6 which positions particular amino acid residues at the channel constriction.
MeSH term(s)	Humans ; Cryoelectron Microscopy ; Membrane Proteins/metabolism ; Temperature ; TRPM Cation Channels/metabolism
Chemical Substances	Membrane Proteins ; TRPM Cation Channels ; TRPM8 protein, human
Language	English
Publishing date	2023-10-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-023-05425-6
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Article ; Online: MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database.

Mazzolari, Angelica / Perazzoni, Pietro / Sabato, Emanuela / Lunghini, Filippo / Beccari, Andrea R / Vistoli, Giulio / Pedretti, Alessandro

International journal of molecular sciences

2023 Volume 24, Issue 13

Abstract: The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate ... ...

Abstract	The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.
MeSH term(s)	Artificial Intelligence ; Databases, Factual ; Chemical Phenomena ; Biotransformation
Language	English
Publishing date	2023-07-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms241311064
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Article ; Online: Computational Insights into the Sequence-Activity Relationships of the NGF(1-14) Peptide by Molecular Dynamics Simulations.

Vittorio, Serena / Manelfi, Candida / Gervasoni, Silvia / Beccari, Andrea R / Pedretti, Alessandro / Vistoli, Giulio / Talarico, Carmine

Cells

2022 Volume 11, Issue 18

Abstract: The Nerve Growth Factor (NGF) belongs to the neurothrophins protein family involved in the survival of neurons in the nervous system. The interaction of NGF with its high-affinity receptor TrkA mediates different cellular pathways related to Alzheimer's ... ...

Abstract	The Nerve Growth Factor (NGF) belongs to the neurothrophins protein family involved in the survival of neurons in the nervous system. The interaction of NGF with its high-affinity receptor TrkA mediates different cellular pathways related to Alzheimer's disease, pain, ocular dysfunction, and cancer. Therefore, targeting NGF-TrkA interaction represents a valuable strategy for the development of new therapeutic agents. In recent years, experimental studies have revealed that peptides belonging to the N-terminal domain of NGF are able to partly mimic the biological activity of the whole protein paving the way towards the development of small peptides that can selectively target specific signaling pathways. Hence, understanding the molecular basis of the interaction between the N-terminal segment of NGF and TrkA is fundamental for the rational design of new peptides mimicking the NGF N-terminal domain. In this study, molecular dynamics simulation, binding free energy calculations and per-residue energy decomposition analysis were combined in order to explore the molecular recognition pattern between the experimentally active NGF(1-14) peptide and TrkA. The results highlighted the importance of His4, Arg9 and Glu11 as crucial residues for the stabilization of NGF(1-14)-TrkA interaction, thus suggesting useful insights for the structure-based design of new therapeutic peptides able to modulate NGF-TrkA interaction.
MeSH term(s)	Molecular Dynamics Simulation ; Nerve Growth Factor/metabolism ; Peptides ; Receptor, trkA/metabolism ; Signal Transduction
Chemical Substances	Peptides ; Nerve Growth Factor (9061-61-4) ; Receptor, trkA (EC 2.7.10.1)
Language	English
Publishing date	2022-09-08
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2661518-6
ISSN	2073-4409 ; 2073-4409
ISSN (online)	2073-4409
ISSN	2073-4409
DOI	10.3390/cells11182808
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Article ; Online: SARS-CoV-2 Entry Inhibitors: Small Molecules and Peptides Targeting Virus or Host Cells.

Cannalire, Rolando / Stefanelli, Irina / Cerchia, Carmen / Beccari, Andrea R / Pelliccia, Sveva / Summa, Vincenzo

International journal of molecular sciences

2020 Volume 21, Issue 16

Abstract: The pandemic evolution of SARS-CoV-2 infection is forcing the scientific community to unprecedented efforts to explore all possible approaches against COVID-19. In this context, targeting virus entry is a promising antiviral strategy for controlling ... ...

Abstract	The pandemic evolution of SARS-CoV-2 infection is forcing the scientific community to unprecedented efforts to explore all possible approaches against COVID-19. In this context, targeting virus entry is a promising antiviral strategy for controlling viral infections. The main strategies pursued to inhibit the viral entry are considering both the virus and the host factors involved in the process. Primarily, direct-acting antivirals rely on inhibition of the interaction between ACE2 and the receptor binding domain (RBD) of the Spike (S) protein or targeting the more conserved heptad repeats (HRs), involved in the membrane fusion process. The inhibition of host TMPRSS2 and cathepsins B/L may represent a complementary strategy to be investigated. In this review, we discuss the development entry inhibitors targeting the S protein, as well as the most promising host targeting strategies involving TMPRSS2 and CatB/L, which have been exploited so far against CoVs and other related viruses.
MeSH term(s)	Angiotensin-Converting Enzyme Inhibitors/pharmacology ; Animals ; Antiviral Agents/pharmacology ; Betacoronavirus/drug effects ; Betacoronavirus/metabolism ; Betacoronavirus/physiology ; Humans ; SARS-CoV-2 ; Serine Proteinase Inhibitors/pharmacology ; Spike Glycoprotein, Coronavirus/metabolism ; Virus Internalization/drug effects
Chemical Substances	Angiotensin-Converting Enzyme Inhibitors ; Antiviral Agents ; Serine Proteinase Inhibitors ; Spike Glycoprotein, Coronavirus
Keywords	covid19
Language	English
Publishing date	2020-08-09
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms21165707
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Article ; Online: Targeting SARS-CoV-2 Proteases and Polymerase for COVID-19 Treatment: State of the Art and Future Opportunities.

Cannalire, Rolando / Cerchia, Carmen / Beccari, Andrea R / Di Leva, Francesco Saverio / Summa, Vincenzo

Journal of medicinal chemistry

2020 Volume 65, Issue 4, Page(s) 2716–2746

Abstract: The newly emerged coronavirus, called SARS-CoV-2, is the causing pathogen of pandemic COVID-19. The identification of drugs to treat COVID-19 and other coronavirus diseases is an urgent global need, thus different strategies targeting either virus or ... ...

Abstract	The newly emerged coronavirus, called SARS-CoV-2, is the causing pathogen of pandemic COVID-19. The identification of drugs to treat COVID-19 and other coronavirus diseases is an urgent global need, thus different strategies targeting either virus or host cell are still under investigation. Direct-acting agents, targeting protease and polymerase functionalities, represent a milestone in antiviral therapy. The 3C-like (or Main) protease (3CL
MeSH term(s)	Antiviral Agents/chemistry ; Antiviral Agents/pharmacology ; COVID-19/metabolism ; Coronavirus 3C Proteases/antagonists & inhibitors ; Coronavirus 3C Proteases/metabolism ; Humans ; Molecular Structure ; Protease Inhibitors/chemistry ; Protease Inhibitors/pharmacology ; RNA-Dependent RNA Polymerase/antagonists & inhibitors ; RNA-Dependent RNA Polymerase/metabolism ; SARS-CoV-2/drug effects ; SARS-CoV-2/enzymology ; COVID-19 Drug Treatment
Chemical Substances	Antiviral Agents ; Protease Inhibitors ; RNA-Dependent RNA Polymerase (EC 2.7.7.48) ; 3C-like proteinase, SARS-CoV-2 (EC 3.4.22.-) ; Coronavirus 3C Proteases (EC 3.4.22.28)
Keywords	covid19
Language	English
Publishing date	2020-11-13
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	218133-2
ISSN	1520-4804 ; 0022-2623
ISSN (online)	1520-4804
ISSN	0022-2623
DOI	10.1021/acs.jmedchem.0c01140
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Article ; Online: Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on The hTRPM8 Channel.

Talarico, Carmine / Gervasoni, Silvia / Manelfi, Candida / Pedretti, Alessandro / Vistoli, Giulio / Beccari, Andrea R

International journal of molecular sciences

2020 Volume 21, Issue 7

Abstract: Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. ...

Abstract	Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures.
MeSH term(s)	Humans ; Models, Molecular ; Molecular Dynamics Simulation ; Protein Binding ; Protein Structure, Quaternary ; Protein Structure, Tertiary ; TRPM Cation Channels/genetics ; TRPM Cation Channels/metabolism
Chemical Substances	TRPM Cation Channels ; TRPM8 protein, human
Language	English
Publishing date	2020-03-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms21072265
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